{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "30fe8578",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T20:26:39.629080Z",
     "start_time": "2023-12-27T20:26:36.921280Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import requests\n",
    "from lxml import etree\n",
    "import re\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import json\n",
    "import copy\n",
    "import sys\n",
    "import seaborn as sns\n",
    "import math\n",
    "import scipy.stats as stats\n",
    "import statistics as ss\n",
    "import time\n",
    "import seaborn as sns\n",
    "import statsmodels.formula.api as smf\n",
    "from collections import defaultdict\n",
    "\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "219a57a3",
   "metadata": {},
   "source": [
    "## caculate the percentage of news sources in references on the pages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "aa45c992",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T20:23:17.253169Z",
     "start_time": "2023-12-26T20:23:17.049862Z"
    }
   },
   "outputs": [],
   "source": [
    "df2 = pd.read_csv('.../referNewsWeb_updated.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3789238f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T20:23:21.246819Z",
     "start_time": "2023-12-26T20:23:21.236565Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51705"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "482656ec",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T20:25:29.252596Z",
     "start_time": "2023-12-26T20:25:29.243113Z"
    }
   },
   "outputs": [],
   "source": [
    "#df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "11f37265",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T20:24:19.821717Z",
     "start_time": "2023-12-26T20:24:19.792887Z"
    }
   },
   "outputs": [],
   "source": [
    "dfScores = pd.read_csv('.../bias_scores.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cd060d1d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T20:24:20.253888Z",
     "start_time": "2023-12-26T20:24:20.240049Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19022"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  media outlets that have a ideological score \n",
    "len(dfScores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "cea12ca4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T20:25:50.408458Z",
     "start_time": "2023-12-26T20:25:50.380842Z"
    }
   },
   "outputs": [],
   "source": [
    "# get the the number of references from sources with known ideologcial leanings \n",
    "dfNews = df2[df2['urlSourceUpdated'].isin(dfScores['domain'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6055329e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T20:26:22.673777Z",
     "start_time": "2023-12-26T20:26:22.661325Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43613"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dfNews)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "316c4937",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T20:27:41.706159Z",
     "start_time": "2023-12-26T20:27:41.692986Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7167178846691098"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# news sources take up 71.67% of all citations \n",
    "43613 / 60851"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5818c7cc",
   "metadata": {},
   "source": [
    "### create a file for source count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "598ff695",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T20:33:37.101705Z",
     "start_time": "2023-12-26T20:33:37.072019Z"
    }
   },
   "outputs": [],
   "source": [
    "# get the sites that are not missing\n",
    "sites = list(df2[df2['urlSourceUpdated'] != 'notFound']['urlSourceUpdated'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3d68fe0f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T20:34:58.436387Z",
     "start_time": "2023-12-26T20:34:58.422371Z"
    }
   },
   "outputs": [],
   "source": [
    "sourceCount = Counter(sites).most_common()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "899757fd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T20:35:05.387246Z",
     "start_time": "2023-12-26T20:35:05.376616Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('house.gov', 3454),\n",
       " ('nytimes.com', 3075),\n",
       " ('washingtonpost.com', 2236),\n",
       " ('thehill.com', 2034),\n",
       " ('politico.com', 1741)]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sourceCount[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0bbd3202",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T20:35:11.209955Z",
     "start_time": "2023-12-26T20:35:11.198023Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2572"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sourceCount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "36cad1ba",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T21:42:27.032652Z",
     "start_time": "2023-12-26T21:42:27.020842Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "source = [i[0] for i in sourceCount]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "85749601",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T21:42:28.248351Z",
     "start_time": "2023-12-26T21:42:28.238031Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "count = [i[1] for i in sourceCount]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "0ac427ce",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T21:42:28.875281Z",
     "start_time": "2023-12-26T21:42:28.866544Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "sourceDict = {'source': source, 'count': count}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "267b1370",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T21:43:24.453654Z",
     "start_time": "2023-12-26T21:43:24.442119Z"
    }
   },
   "outputs": [],
   "source": [
    "dfCount = pd.DataFrame(sourceDict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "56d6cb9f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T21:43:26.662124Z",
     "start_time": "2023-12-26T21:43:26.648396Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "# count/sum\n",
    "decimal = []\n",
    "allCount = sum(list(dfCount['count']))\n",
    "for i in list(dfCount['count']):\n",
    "    quotient = round(i/allCount, 4) # len(source1) is 9304\n",
    "    decimal.append(quotient)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "eb63a5b9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T21:43:27.224173Z",
     "start_time": "2023-12-26T21:43:27.214001Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "# creat a new column\n",
    "dfCount['count/sum'] = decimal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "71a09c20",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T21:43:28.351560Z",
     "start_time": "2023-12-26T21:43:28.340253Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "percent = []\n",
    "for i in decimal:\n",
    "    per = \"{:.2%}\".format(i) # save the number with two decimal places\n",
    "    percent.append(per)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "a04b3073",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T21:43:28.933967Z",
     "start_time": "2023-12-26T21:43:28.924030Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "dfCount['percent'] = percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b8434bdb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T21:43:29.765659Z",
     "start_time": "2023-12-26T21:43:29.748954Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>count</th>\n",
       "      <th>count/sum</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>house.gov</td>\n",
       "      <td>3454</td>\n",
       "      <td>0.0675</td>\n",
       "      <td>6.75%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nytimes.com</td>\n",
       "      <td>3075</td>\n",
       "      <td>0.0601</td>\n",
       "      <td>6.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>washingtonpost.com</td>\n",
       "      <td>2236</td>\n",
       "      <td>0.0437</td>\n",
       "      <td>4.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>thehill.com</td>\n",
       "      <td>2034</td>\n",
       "      <td>0.0398</td>\n",
       "      <td>3.98%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>politico.com</td>\n",
       "      <td>1741</td>\n",
       "      <td>0.0340</td>\n",
       "      <td>3.40%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               source  count  count/sum percent\n",
       "0           house.gov   3454     0.0675   6.75%\n",
       "1         nytimes.com   3075     0.0601   6.01%\n",
       "2  washingtonpost.com   2236     0.0437   4.37%\n",
       "3         thehill.com   2034     0.0398   3.98%\n",
       "4        politico.com   1741     0.0340   3.40%"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfCount.iloc[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feeed8d0",
   "metadata": {},
   "source": [
    "### identify news outlest with known political leanings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "ebb2597b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T21:48:54.584285Z",
     "start_time": "2023-12-26T21:48:54.561402Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "# return 999 if the key does not exist \n",
    "def returnNull():\n",
    "    return(999)\n",
    "\n",
    "# Create a defaultdict\n",
    "mediaScore = defaultdict(returnNull)\n",
    "\n",
    "# Add keys and values to the defaultdict\n",
    "for k, v in zip(dfScores['domain'], dfScores['score']):\n",
    "    mediaScore[k] = v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "546afb89",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T21:49:06.083660Z",
     "start_time": "2023-12-26T21:49:06.068581Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "# get the bias scores based on domains\n",
    "scoreList = []\n",
    "for domain in dfCount['source']: \n",
    "    v = mediaScore[domain]\n",
    "    scoreList.append(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "bd8e2bbd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T21:49:09.333751Z",
     "start_time": "2023-12-26T21:49:09.322366Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "dfCount['biaScore'] = scoreList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "83e6b920",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T21:49:12.861090Z",
     "start_time": "2023-12-26T21:49:12.845527Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1167"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1167 media outlets have unidentified political leanings \n",
    "len(dfCount[dfCount['biaScore'] == 999])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "12c7d3fd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T21:49:28.203413Z",
     "start_time": "2023-12-26T21:49:28.190724Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "dfCountValid = dfCount[dfCount['biaScore'] != 999]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "12d6ee87",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T21:49:28.837835Z",
     "start_time": "2023-12-26T21:49:28.820946Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>count</th>\n",
       "      <th>count/sum</th>\n",
       "      <th>percent</th>\n",
       "      <th>biaScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>house.gov</td>\n",
       "      <td>3454</td>\n",
       "      <td>0.0675</td>\n",
       "      <td>6.75%</td>\n",
       "      <td>-0.4210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nytimes.com</td>\n",
       "      <td>3075</td>\n",
       "      <td>0.0601</td>\n",
       "      <td>6.01%</td>\n",
       "      <td>-0.2602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>washingtonpost.com</td>\n",
       "      <td>2236</td>\n",
       "      <td>0.0437</td>\n",
       "      <td>4.37%</td>\n",
       "      <td>-0.2342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>thehill.com</td>\n",
       "      <td>2034</td>\n",
       "      <td>0.0398</td>\n",
       "      <td>3.98%</td>\n",
       "      <td>-0.0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>politico.com</td>\n",
       "      <td>1741</td>\n",
       "      <td>0.0340</td>\n",
       "      <td>3.40%</td>\n",
       "      <td>-0.1914</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               source  count  count/sum percent  biaScore\n",
       "0           house.gov   3454     0.0675   6.75%   -0.4210\n",
       "1         nytimes.com   3075     0.0601   6.01%   -0.2602\n",
       "2  washingtonpost.com   2236     0.0437   4.37%   -0.2342\n",
       "3         thehill.com   2034     0.0398   3.98%   -0.0600\n",
       "4        politico.com   1741     0.0340   3.40%   -0.1914"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfCountValid.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "04336aa9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T21:51:33.363400Z",
     "start_time": "2023-12-26T21:51:33.352606Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "dfNewSort = dfCountValid.sort_values('biaScore', ascending = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "df7405ab",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T21:51:34.080223Z",
     "start_time": "2023-12-26T21:51:34.071618Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "dfNewSort.reset_index(drop = True, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "6c7648c8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T21:51:34.535941Z",
     "start_time": "2023-12-26T21:51:34.524067Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1405"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dfNewSort)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "8f604921",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T21:52:17.947874Z",
     "start_time": "2023-12-26T21:52:17.933161Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "681"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the number of conservative outlets\n",
    "len(dfNewSort[dfNewSort['biaScore'] > 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "6547024d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T21:52:33.875725Z",
     "start_time": "2023-12-26T21:52:33.862211Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "724"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the number of liberal outlets\n",
    "len(dfNewSort[dfNewSort['biaScore'] < 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f1d4047",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9f5506b1",
   "metadata": {},
   "source": [
    "### plot a scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "fc54c419",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T22:02:16.150113Z",
     "start_time": "2023-12-26T22:02:16.131802Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('.../categoryCountUpdated4.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "c79ff973",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T22:02:47.329857Z",
     "start_time": "2023-12-26T22:02:47.148293Z"
    }
   },
   "outputs": [],
   "source": [
    "dfNews = pd.read_csv('.../referNewsWebUpdatedIdentified_updated.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "258be218",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T22:03:51.925237Z",
     "start_time": "2023-12-26T22:03:51.904271Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "# count the references\n",
    "dfNewsIde = dfNews.groupby('name')['name'].count().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "d951d4d4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T22:03:52.709148Z",
     "start_time": "2023-12-26T22:03:52.700584Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "dfNewsIde.rename(columns = {'name': 'newsPoliCount'}, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "8289f8a0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T22:03:53.305941Z",
     "start_time": "2023-12-26T22:03:53.298057Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "dfNewsIde.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "243b1c61",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T22:04:15.767041Z",
     "start_time": "2023-12-26T22:04:15.751010Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>newsPoliCountUpdated2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aaron_Bean</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aaron_Schock</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abby_Finkenauer</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abigail_Spanberger</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adam_Kinzinger</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name  newsPoliCountUpdated2\n",
       "0          Aaron_Bean                      7\n",
       "1        Aaron_Schock                    134\n",
       "2     Abby_Finkenauer                     21\n",
       "3  Abigail_Spanberger                     47\n",
       "4      Adam_Kinzinger                     92"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfNewsIde.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "30f0cd63",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T22:04:04.078863Z",
     "start_time": "2023-12-26T22:04:04.067620Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "dfNewsIde.rename(columns = {'newsPoliCount': 'newsPoliCountUpdated2'}, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "8d12fe9b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T22:06:17.994587Z",
     "start_time": "2023-12-26T22:06:17.973728Z"
    }
   },
   "outputs": [],
   "source": [
    "dfTotal = pd.merge(df, dfNewsIde, on = 'name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "f16640cd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T22:06:27.495739Z",
     "start_time": "2023-12-26T22:06:27.483855Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>totalCount</th>\n",
       "      <th>newsCount</th>\n",
       "      <th>webCount</th>\n",
       "      <th>bookCount</th>\n",
       "      <th>newsgroupCount</th>\n",
       "      <th>magazineCount</th>\n",
       "      <th>journalCount</th>\n",
       "      <th>interviewCount</th>\n",
       "      <th>conferenceCount</th>\n",
       "      <th>...</th>\n",
       "      <th>thesisCount</th>\n",
       "      <th>speechCount</th>\n",
       "      <th>episodeCount</th>\n",
       "      <th>mediaCount</th>\n",
       "      <th>mediaNoCount</th>\n",
       "      <th>checkCount</th>\n",
       "      <th>newsWebCount</th>\n",
       "      <th>newsPoliCount</th>\n",
       "      <th>newsPoliUpdate</th>\n",
       "      <th>newsPoliCountUpdated2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chuck_Grassley</td>\n",
       "      <td>173</td>\n",
       "      <td>82</td>\n",
       "      <td>86</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>163</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mitch_McConnell</td>\n",
       "      <td>245</td>\n",
       "      <td>130</td>\n",
       "      <td>80</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>245</td>\n",
       "      <td>198</td>\n",
       "      <td>146.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dianne_Feinstein</td>\n",
       "      <td>145</td>\n",
       "      <td>84</td>\n",
       "      <td>54</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>126</td>\n",
       "      <td>59.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Patty_Murray</td>\n",
       "      <td>76</td>\n",
       "      <td>36</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>56</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ron_Wyden</td>\n",
       "      <td>161</td>\n",
       "      <td>87</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>161</td>\n",
       "      <td>142</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               name  totalCount  newsCount  webCount  bookCount  \\\n",
       "0    Chuck_Grassley         173         82        86          2   \n",
       "1   Mitch_McConnell         245        130        80         26   \n",
       "2  Dianne_Feinstein         145         84        54          4   \n",
       "3      Patty_Murray          76         36        35          1   \n",
       "4         Ron_Wyden         161         87        71          0   \n",
       "\n",
       "   newsgroupCount  magazineCount  journalCount  interviewCount  \\\n",
       "0               0              1             0               0   \n",
       "1               0              5             4               0   \n",
       "2               0              2             0               0   \n",
       "3               0              2             1               0   \n",
       "4               0              3             0               0   \n",
       "\n",
       "   conferenceCount  ...  thesisCount  speechCount  episodeCount  mediaCount  \\\n",
       "0                0  ...            0            0             0           0   \n",
       "1                0  ...            0            0             0           0   \n",
       "2                0  ...            0            0             0           1   \n",
       "3                0  ...            0            0             0           0   \n",
       "4                0  ...            0            0             0           0   \n",
       "\n",
       "   mediaNoCount  checkCount  newsWebCount  newsPoliCount  newsPoliUpdate  \\\n",
       "0             0         172           163           90.0            90.0   \n",
       "1             0         245           198          146.0           146.0   \n",
       "2             0         144           126           59.0            59.0   \n",
       "3             0          76            56           21.0            21.0   \n",
       "4             0         161           142           75.0            75.0   \n",
       "\n",
       "   newsPoliCountUpdated2  \n",
       "0                    147  \n",
       "1                    182  \n",
       "2                    113  \n",
       "3                     54  \n",
       "4                    132  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTotal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "00655a15",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:00:12.164249Z",
     "start_time": "2023-12-27T01:00:12.151262Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "# get the percent of news citations (from outlets with known poli leanings) for individual pages\n",
    "percentIde = dfTotal['newsPoliCountUpdated2'].values / dfTotal['totalCount'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "2f5d3a1d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:00:16.368678Z",
     "start_time": "2023-12-27T01:00:16.352316Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "# # round to two decimal places\n",
    "percentIde2 = [round(i, 2) for i in percentIde]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "c7e48fa8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:00:20.982824Z",
     "start_time": "2023-12-27T01:00:20.971009Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "# create a new column\n",
    "dfTotal['percentNewsIde'] = percentIde2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "72cd7158",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-04T19:26:03.526651Z",
     "start_time": "2023-07-04T19:26:03.518927Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "#df[df['percentNewsIde'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "1c54b520",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:01:18.973764Z",
     "start_time": "2023-12-27T01:01:18.959594Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "# create a new dataframe\n",
    "dfPlot = dfTotal[['name', 'totalCount', 'newsPoliCountUpdated2','percentNewsIde']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "dfca114f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:01:22.182053Z",
     "start_time": "2023-12-27T01:01:22.166128Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>totalCount</th>\n",
       "      <th>newsPoliCountUpdated2</th>\n",
       "      <th>percentNewsIde</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chuck_Grassley</td>\n",
       "      <td>173</td>\n",
       "      <td>147</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mitch_McConnell</td>\n",
       "      <td>245</td>\n",
       "      <td>182</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dianne_Feinstein</td>\n",
       "      <td>145</td>\n",
       "      <td>113</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Patty_Murray</td>\n",
       "      <td>76</td>\n",
       "      <td>54</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ron_Wyden</td>\n",
       "      <td>161</td>\n",
       "      <td>132</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               name  totalCount  newsPoliCountUpdated2  percentNewsIde\n",
       "0    Chuck_Grassley         173                    147            0.85\n",
       "1   Mitch_McConnell         245                    182            0.74\n",
       "2  Dianne_Feinstein         145                    113            0.78\n",
       "3      Patty_Murray          76                     54            0.71\n",
       "4         Ron_Wyden         161                    132            0.82"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfPlot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "e7f87990",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:02:39.389397Z",
     "start_time": "2023-12-27T01:02:39.377202Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6831085714285715"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the average percent of news sources (with known poli leanings) in references\n",
    "sum(dfTotal['percentNewsIde']) / len(dfPlot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "06ca0f12",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:04:54.845608Z",
     "start_time": "2023-12-27T01:04:54.825733Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "dfMembers = pd.read_csv('.../congressMembersUrlUpdated4.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "579a82ac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:05:09.446172Z",
     "start_time": "2023-12-27T01:05:09.432563Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Republican\n",
       "2      Democratic\n",
       "19    Independent\n",
       "Name: party, dtype: object"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfMembers['party'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "3671a388",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:05:17.714188Z",
     "start_time": "2023-12-27T01:05:17.702303Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "dfMem = dfMembers[['newNames', 'party']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "2445030e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:05:18.537996Z",
     "start_time": "2023-12-27T01:05:18.524088Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>newNames</th>\n",
       "      <th>party</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chuck_Grassley</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mitch_McConnell</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dianne_Feinstein</td>\n",
       "      <td>Democratic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Patty_Murray</td>\n",
       "      <td>Democratic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ron_Wyden</td>\n",
       "      <td>Democratic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           newNames       party\n",
       "0    Chuck_Grassley  Republican\n",
       "1   Mitch_McConnell  Republican\n",
       "2  Dianne_Feinstein  Democratic\n",
       "3      Patty_Murray  Democratic\n",
       "4         Ron_Wyden  Democratic"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfMem.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "e57256c6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:05:38.960677Z",
     "start_time": "2023-12-27T01:05:38.947393Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "# merge two datasets on the column of names\n",
    "dfRev = pd.merge(dfMem, dfPlot, left_on = 'newNames', right_on = 'name').drop('newNames', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "58ab9e5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:05:41.317134Z",
     "start_time": "2023-12-27T01:05:41.297728Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>party</th>\n",
       "      <th>name</th>\n",
       "      <th>totalCount</th>\n",
       "      <th>newsPoliCountUpdated2</th>\n",
       "      <th>percentNewsIde</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Republican</td>\n",
       "      <td>Chuck_Grassley</td>\n",
       "      <td>173</td>\n",
       "      <td>147</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Republican</td>\n",
       "      <td>Mitch_McConnell</td>\n",
       "      <td>245</td>\n",
       "      <td>182</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Democratic</td>\n",
       "      <td>Dianne_Feinstein</td>\n",
       "      <td>145</td>\n",
       "      <td>113</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Democratic</td>\n",
       "      <td>Patty_Murray</td>\n",
       "      <td>76</td>\n",
       "      <td>54</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Democratic</td>\n",
       "      <td>Ron_Wyden</td>\n",
       "      <td>161</td>\n",
       "      <td>132</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        party              name  totalCount  newsPoliCountUpdated2  \\\n",
       "0  Republican    Chuck_Grassley         173                    147   \n",
       "1  Republican   Mitch_McConnell         245                    182   \n",
       "2  Democratic  Dianne_Feinstein         145                    113   \n",
       "3  Democratic      Patty_Murray          76                     54   \n",
       "4  Democratic         Ron_Wyden         161                    132   \n",
       "\n",
       "   percentNewsIde  \n",
       "0            0.85  \n",
       "1            0.74  \n",
       "2            0.78  \n",
       "3            0.71  \n",
       "4            0.82  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfRev.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "ba842277",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:07:38.934471Z",
     "start_time": "2023-12-27T01:07:38.921587Z"
    }
   },
   "outputs": [],
   "source": [
    "dfRep = dfRev[dfRev['party'] == 'Republican']\n",
    "dfLib = dfRev[dfRev['party'] == 'Democratic']\n",
    "#dfInd = dfRev[dfRev['party'] == 'Independent']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "3fa4812f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:08:36.422748Z",
     "start_time": "2023-12-27T01:08:36.015360Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add a legend\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (10, 6))\n",
    "#y = dfRev['percentNewsIde']\n",
    "#x = range(len(y))\n",
    "\n",
    "#colors = np.random.rand(532)\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "#plt.scatter(range(len(dfLib)), dfLib['percentNewsIde'],  c = 'blue', label = 'Pages for Democrats', alpha = 0.8)  \n",
    "plt.scatter(range(len(dfRep)), dfRep['percentNewsIde'], c = 'red', label = 'Pages for Republicans', alpha = 0.8)   \n",
    "plt.scatter(range(len(dfLib)), dfLib['percentNewsIde'],  c = 'blue', label = 'Pages for Democrats', alpha = 0.8)   \n",
    "#plt.scatter(range(len(dfInd)), dfInd['percentNewsIde'], c = 'purple',label = 'Pages for Independents', alpha = 0.8)   \n",
    "\n",
    "plt.legend(loc='lower left')\n",
    "\n",
    "plt.xticks([])\n",
    "plt.yticks(np.arange(0,1.1,0.1))\n",
    "plt.xlabel('Wikipedia Pages')\n",
    "plt.ylabel('Percentage of News Sources in References')\n",
    "plt.axhline(0.68, linestyle = '--', color = 'k')\n",
    "#plt.savefig('.../scatterRevelanceNews_updated.pdf', bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9051a566",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8062e25f",
   "metadata": {},
   "source": [
    "## caculate the frequency of liberal and conservative news sources on the pages"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c742085",
   "metadata": {},
   "source": [
    "### perform a t test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "977436e6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:12:56.973657Z",
     "start_time": "2023-12-27T01:12:56.957692Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "dfTable2 = pd.read_csv('.../tTestTable.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "c5b56da4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:12:58.182527Z",
     "start_time": "2023-12-27T01:12:58.168892Z"
    },
    "run_control": {
     "marked": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>blue</th>\n",
       "      <th>red</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aaron_Bean</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aaron_Schock</td>\n",
       "      <td>100</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abby_Finkenauer</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abigail_Spanberger</td>\n",
       "      <td>39</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adam_Kinzinger</td>\n",
       "      <td>83</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name  blue  red\n",
       "0          Aaron_Bean     1    6\n",
       "1        Aaron_Schock   100   34\n",
       "2     Abby_Finkenauer    11   10\n",
       "3  Abigail_Spanberger    39    8\n",
       "4      Adam_Kinzinger    83    9"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTable2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "7a38766f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:15:24.587178Z",
     "start_time": "2023-12-27T01:15:24.571715Z"
    }
   },
   "outputs": [],
   "source": [
    "dfTable2 = dfTable2[dfTable2['name'].isin(dfMembers['newNames'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "907d807c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:15:27.231328Z",
     "start_time": "2023-12-27T01:15:27.223683Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "875"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dfTable2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "d79dc8ef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:15:29.564726Z",
     "start_time": "2023-12-27T01:15:29.553261Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33846"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTable2['blue'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "7bc3984b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:15:30.843016Z",
     "start_time": "2023-12-27T01:15:30.828949Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9767"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTable2['red'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "39224b5e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:15:36.523537Z",
     "start_time": "2023-12-27T01:15:36.508713Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TtestResult(statistic=18.64230446828503, pvalue=6.9003567495151004e-71, df=1748.0)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# independent t test\n",
    "stats.ttest_ind(dfTable2['blue'], \n",
    "                dfTable2['red'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "4d2bad63",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:15:44.806110Z",
     "start_time": "2023-12-27T01:15:44.793118Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TtestResult(statistic=18.64230446828503, pvalue=1.754529662017703e-67, df=1082.375358403943)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# welch t test\n",
    "stats.ttest_ind(dfTable2['blue'], dfTable2['red'], equal_var = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "f67e14cf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:15:49.930560Z",
     "start_time": "2023-12-27T01:15:49.918708Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.7553125619888306, pvalue=4.623322137539754e-34)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampling_difference = dfTable2['blue'].values - \\\n",
    "                     dfTable2['red'].values\n",
    "\n",
    "stats.shapiro(sampling_difference)\n",
    "#Unfortunately, the output is not labelled but is in the format of (W test statistic, p-value). \n",
    "# The test is not significant which indicates the sampling distribution is normally distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "7710012a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T01:15:51.752637Z",
     "start_time": "2023-12-27T01:15:51.736454Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeveneResult(statistic=253.7166053261225, pvalue=1.9177138857007143e-53)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.levene(dfTable2['blue'],\n",
    "             dfTable2['red'],\n",
    "             center= 'mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01db97c6",
   "metadata": {},
   "source": [
    "### plot a stacked bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e578a3ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T20:26:46.897727Z",
     "start_time": "2023-12-27T20:26:46.881167Z"
    }
   },
   "outputs": [],
   "source": [
    "dfMem = dfMembers[['newNames', 'jobTitles', 'party']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "95bede84",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T20:26:49.034595Z",
     "start_time": "2023-12-27T20:26:49.015604Z"
    }
   },
   "outputs": [],
   "source": [
    "# merge two datasets\n",
    "dfTable3 = pd.merge(dfMem, dfTable2, left_on= 'newNames', right_on = 'name', how = 'inner' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "befe39ca",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T20:26:49.922833Z",
     "start_time": "2023-12-27T20:26:49.911790Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "875"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dfTable3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eb1a1a50",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T20:26:51.393873Z",
     "start_time": "2023-12-27T20:26:51.382084Z"
    }
   },
   "outputs": [],
   "source": [
    "# remove independents; note that I skipped dfTable4\n",
    "dfTable5 = dfTable3[dfTable3['party'] != 'Independent'].reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a960c22c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T20:26:52.517494Z",
     "start_time": "2023-12-27T20:26:52.505558Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "872"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dfTable5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7e221c89",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T20:26:54.105789Z",
     "start_time": "2023-12-27T20:26:54.081477Z"
    }
   },
   "outputs": [],
   "source": [
    "# assign values \n",
    "def assignValue(df):\n",
    "    if df['party'] == 'Democratic' and df['jobTitles'] == 'represent':\n",
    "        return 0  # assign 0 to reprsentatives among democrats\n",
    "    elif df['party'] == 'Democratic' and df['jobTitles'] == 'senator':\n",
    "        return 1  # assign 1 to senator among democrats\n",
    "    elif df['party'] == 'Republican' and df['jobTitles'] == 'represent':\n",
    "        return 2 # assign 2 to representatives among republicans\n",
    "    elif df['party'] == 'Republican' and df['jobTitles'] == 'senator':\n",
    "        return 3  # assign 3 to senators among republicans \n",
    "\n",
    "# apply function to each row axis = 1\n",
    "dfTable5['plotCategory'] = dfTable5.apply(assignValue, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3eba24c2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T20:26:55.956249Z",
     "start_time": "2023-12-27T20:26:55.943550Z"
    }
   },
   "outputs": [],
   "source": [
    "dfTable6 = dfTable5.sort_values('plotCategory').reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "36fa60f6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T20:26:58.561085Z",
     "start_time": "2023-12-27T20:26:58.549450Z"
    }
   },
   "outputs": [],
   "source": [
    "dfTable6['sumBlueRed'] = dfTable6['blue'] + dfTable6['red']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9c5ddecb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T20:27:25.540669Z",
     "start_time": "2023-12-27T20:27:25.521430Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>newNames</th>\n",
       "      <th>jobTitles</th>\n",
       "      <th>party</th>\n",
       "      <th>name</th>\n",
       "      <th>blue</th>\n",
       "      <th>red</th>\n",
       "      <th>plotCategory</th>\n",
       "      <th>sumBlueRed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>Mitt_Romney</td>\n",
       "      <td>senator</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Mitt_Romney</td>\n",
       "      <td>347</td>\n",
       "      <td>74</td>\n",
       "      <td>3</td>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>Marjorie_Taylor_Greene</td>\n",
       "      <td>represent</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Marjorie_Taylor_Greene</td>\n",
       "      <td>295</td>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>Ted_Cruz</td>\n",
       "      <td>senator</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Ted_Cruz</td>\n",
       "      <td>267</td>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>Kamala_Harris</td>\n",
       "      <td>senator</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Kamala_Harris</td>\n",
       "      <td>253</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>Rand_Paul</td>\n",
       "      <td>senator</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Rand_Paul</td>\n",
       "      <td>208</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Alexandria_Ocasio-Cortez</td>\n",
       "      <td>represent</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Alexandria_Ocasio-Cortez</td>\n",
       "      <td>245</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>Nancy_Pelosi</td>\n",
       "      <td>represent</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Nancy_Pelosi</td>\n",
       "      <td>232</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>Ron_DeSantis</td>\n",
       "      <td>represent</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Ron_DeSantis</td>\n",
       "      <td>133</td>\n",
       "      <td>122</td>\n",
       "      <td>2</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>Matt_Gaetz</td>\n",
       "      <td>represent</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Matt_Gaetz</td>\n",
       "      <td>199</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>Jeff_Sessions</td>\n",
       "      <td>senator</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Jeff_Sessions</td>\n",
       "      <td>215</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     newNames  jobTitles       party  \\\n",
       "814               Mitt_Romney    senator  Republican   \n",
       "465    Marjorie_Taylor_Greene  represent  Republican   \n",
       "811                  Ted_Cruz    senator  Republican   \n",
       "341             Kamala_Harris    senator  Democratic   \n",
       "829                 Rand_Paul    senator  Republican   \n",
       "42   Alexandria_Ocasio-Cortez  represent  Democratic   \n",
       "229              Nancy_Pelosi  represent  Democratic   \n",
       "737              Ron_DeSantis  represent  Republican   \n",
       "446                Matt_Gaetz  represent  Republican   \n",
       "853             Jeff_Sessions    senator  Republican   \n",
       "\n",
       "                         name  blue  red  plotCategory  sumBlueRed  \n",
       "814               Mitt_Romney   347   74             3         421  \n",
       "465    Marjorie_Taylor_Greene   295   53             2         348  \n",
       "811                  Ted_Cruz   267   60             3         327  \n",
       "341             Kamala_Harris   253   41             1         294  \n",
       "829                 Rand_Paul   208   82             3         290  \n",
       "42   Alexandria_Ocasio-Cortez   245   43             0         288  \n",
       "229              Nancy_Pelosi   232   45             0         277  \n",
       "737              Ron_DeSantis   133  122             2         255  \n",
       "446                Matt_Gaetz   199   50             2         249  \n",
       "853             Jeff_Sessions   215   31             3         246  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I annotated the figure with texts based on the data below\n",
    "dfTable6.sort_values('sumBlueRed', ascending = False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "260e2c82",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T02:10:55.797265Z",
     "start_time": "2023-12-27T02:10:55.782648Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "330"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dfTable6[dfTable6['plotCategory'] == 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "ddcc4a3e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T02:11:17.016137Z",
     "start_time": "2023-12-27T02:11:16.998667Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dfTable6[dfTable6['plotCategory'] == 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "5d266e24",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T02:11:49.579086Z",
     "start_time": "2023-12-27T02:11:49.564605Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "405"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dfTable6[dfTable6['plotCategory'] == 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "77777d49",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T02:11:50.657323Z",
     "start_time": "2023-12-27T02:11:50.640480Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dfTable6[dfTable6['plotCategory'] == 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "87c7e9bc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T02:12:13.344627Z",
     "start_time": "2023-12-27T02:12:13.331969Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "872"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "330+60+405+77 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45896450",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 330 reprsent democrats\n",
    "# 60 senator democrats\n",
    "# 405 represent republican\n",
    "# 77 senator republican \n",
    "#dfTable6[dfTable6['plotCategory'] == 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "a7ba100f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T02:14:05.850469Z",
     "start_time": "2023-12-27T02:14:04.289954Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# democrats on the left; republicans on the right\n",
    "fig, ax = plt.subplots(figsize = (15, 8))\n",
    "\n",
    "names = list(dfTable6['newNames'])  \n",
    "blueSources = list(dfTable6['blue'])\n",
    "redSources = list(dfTable6['red'])\n",
    "\n",
    "# Calculate the positions for the bars for both layers\n",
    "x = np.arange(len(names))\n",
    "\n",
    "# Create the first layer of bars\n",
    "plt.bar(x, blueSources, label= 'Liberal Media Sources') #  color = 'blue'\n",
    "# Create the second layer of bars on top of the first layer\n",
    "plt.bar(x, redSources, bottom= blueSources, label='Conservative Media Sources', color = 'red')\n",
    "\n",
    "# Add labels \n",
    "plt.xlabel('')\n",
    "plt.ylabel('Frequency of News Citations', fontsize = 14)\n",
    "# 0-334 reprsent democrats; 334-394 senator democrats; 394 -803; the rest senator republican \n",
    "ax.axvline(x=330, linestyle='--', color='k')\n",
    "ax.axvline(x=390, linestyle='--', color='k')\n",
    "ax.axvline(x=795, linestyle='--', color='k')\n",
    "\n",
    "# add a legend\n",
    "plt.legend(fontsize = 14)\n",
    "# remove xticks \n",
    "plt.xticks([])\n",
    "plt.yticks(np.arange(0,360,50))\n",
    "#remove spines\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "# Show the plot or save it to a file\n",
    "#plt.savefig('.../frequencyCitations4.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d112eed3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2dddd781",
   "metadata": {},
   "source": [
    "#### separate democrats and republicans "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbdc534e",
   "metadata": {},
   "source": [
    "##### pages for democrats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "37aa8aa6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T02:16:55.443804Z",
     "start_time": "2023-12-27T02:16:55.425366Z"
    }
   },
   "outputs": [],
   "source": [
    "dfTable_dem = dfTable6[dfTable6['party'] == 'Democratic']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "750ef25a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T02:17:08.154412Z",
     "start_time": "2023-12-27T02:17:08.132102Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>newNames</th>\n",
       "      <th>jobTitles</th>\n",
       "      <th>party</th>\n",
       "      <th>name</th>\n",
       "      <th>blue</th>\n",
       "      <th>red</th>\n",
       "      <th>plotCategory</th>\n",
       "      <th>sumBlueRed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ann_Kirkpatrick</td>\n",
       "      <td>represent</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Ann_Kirkpatrick</td>\n",
       "      <td>36</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mikie_Sherrill</td>\n",
       "      <td>represent</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Mikie_Sherrill</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Elissa_Slotkin</td>\n",
       "      <td>represent</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Elissa_Slotkin</td>\n",
       "      <td>34</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ted_Deutch</td>\n",
       "      <td>represent</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Ted_Deutch</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>John_Garamendi</td>\n",
       "      <td>represent</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>John_Garamendi</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          newNames  jobTitles       party             name  blue  red  \\\n",
       "0  Ann_Kirkpatrick  represent  Democratic  Ann_Kirkpatrick    36   32   \n",
       "1   Mikie_Sherrill  represent  Democratic   Mikie_Sherrill    38    4   \n",
       "2   Elissa_Slotkin  represent  Democratic   Elissa_Slotkin    34    7   \n",
       "3       Ted_Deutch  represent  Democratic       Ted_Deutch    15   10   \n",
       "4   John_Garamendi  represent  Democratic   John_Garamendi    27    5   \n",
       "\n",
       "   plotCategory  sumBlueRed  \n",
       "0             0          68  \n",
       "1             0          42  \n",
       "2             0          41  \n",
       "3             0          25  \n",
       "4             0          32  "
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTable_dem.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "4a7ee930",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T02:19:42.877003Z",
     "start_time": "2023-12-27T02:19:41.744020Z"
    }
   },
   "outputs": [
    {
     "data": {
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dWvg7TQbb7r///rjnnnvixo0bU9//+uuv44EHHpj5Nzs7O7GzszP1vXvvvberIgIAAADQoCafRrq9vR1nz56Na9euTX3/2rVrU8tKAQAAACClJjPbIiIuXrwYzz33XDz22GPx+OOPxzvvvBPXr1+PF154IXfRAAAAAGhUs8G2Z555Jr755pt47bXX4quvvopHH300Pvjgg3j44YdzFw0AAACARjX5NFIAAAAAyKHJPdsAAAAAIAfBNgAAAABIRLANAAAAABIRbAMAAACARATbAAAAACARwTYAAAAASESwDQAAAAASEWwDAAAAgEQE2wAAAAAgEcE2AAAAAEhEsA0AAAAAEvl/ghlZXHw6wboAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# democrats on the left; republicans on the right\n",
    "fig, ax = plt.subplots(figsize = (15, 8))\n",
    "\n",
    "names = list(dfTable_dem['newNames'])  \n",
    "blueSources = list(dfTable_dem['blue'])\n",
    "redSources = list(dfTable_dem['red'])\n",
    "\n",
    "# Calculate the positions for the bars for both layers\n",
    "x = np.arange(len(names))\n",
    "\n",
    "# Create the first layer of bars\n",
    "plt.bar(x, blueSources, label= 'Liberal Media Sources') #  color = 'blue'\n",
    "# Create the second layer of bars on top of the first layer\n",
    "plt.bar(x, redSources, bottom= blueSources, label='Conservative Media Sources', color = 'red')\n",
    "\n",
    "# Add labels \n",
    "plt.xlabel('')\n",
    "plt.ylabel('Frequency of News Citations', fontsize = 14)\n",
    "# 0-334 reprsent democrats; 334-394 senator democrats; 394 -803; the rest senator republican \n",
    "ax.axvline(x=330, linestyle='--', color='k')\n",
    "#ax.axvline(x=390, linestyle='--', color='k')\n",
    "#ax.axvline(x=795, linestyle='--', color='k')\n",
    "\n",
    "# add a legend\n",
    "plt.legend(fontsize = 14)\n",
    "# remove xticks \n",
    "plt.xticks([])\n",
    "plt.yticks(np.arange(0,360,50))\n",
    "#remove spines\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "# Show the plot or save it to a file\n",
    "#plt.savefig('.../frequencyCitations_dem.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1165af18",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f6962178",
   "metadata": {},
   "source": [
    "##### pages for republicans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "48c9d7a1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T02:20:41.064923Z",
     "start_time": "2023-12-27T02:20:41.051742Z"
    }
   },
   "outputs": [],
   "source": [
    "dfTable_rep = dfTable6[dfTable6['party'] == 'Republican']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "c339f6b9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T02:20:52.663090Z",
     "start_time": "2023-12-27T02:20:52.649168Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "482"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dfTable_rep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "9dbac357",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T02:20:41.771859Z",
     "start_time": "2023-12-27T02:20:41.754014Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>newNames</th>\n",
       "      <th>jobTitles</th>\n",
       "      <th>party</th>\n",
       "      <th>name</th>\n",
       "      <th>blue</th>\n",
       "      <th>red</th>\n",
       "      <th>plotCategory</th>\n",
       "      <th>sumBlueRed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>Mary_Miller</td>\n",
       "      <td>represent</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Mary_Miller</td>\n",
       "      <td>48</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>Debbie_Lesko</td>\n",
       "      <td>represent</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Debbie_Lesko</td>\n",
       "      <td>32</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>Cory_Mills</td>\n",
       "      <td>represent</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Cory_Mills</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>Max_Miller</td>\n",
       "      <td>represent</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Max_Miller</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>Mariannette_Miller-Meeks</td>\n",
       "      <td>represent</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Mariannette_Miller-Meeks</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     newNames  jobTitles       party  \\\n",
       "390               Mary_Miller  represent  Republican   \n",
       "391              Debbie_Lesko  represent  Republican   \n",
       "392                Cory_Mills  represent  Republican   \n",
       "393                Max_Miller  represent  Republican   \n",
       "394  Mariannette_Miller-Meeks  represent  Republican   \n",
       "\n",
       "                         name  blue  red  plotCategory  sumBlueRed  \n",
       "390               Mary_Miller    48   13             2          61  \n",
       "391              Debbie_Lesko    32   24             2          56  \n",
       "392                Cory_Mills     7    4             2          11  \n",
       "393                Max_Miller    19    3             2          22  \n",
       "394  Mariannette_Miller-Meeks    15    8             2          23  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTable_rep.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "b13cdf07",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T02:23:47.300335Z",
     "start_time": "2023-12-27T02:23:45.968635Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# democrats on the left; republicans on the right\n",
    "fig, ax = plt.subplots(figsize = (15, 8))\n",
    "\n",
    "names = list(dfTable_rep['newNames'])  \n",
    "blueSources = list(dfTable_rep['blue'])\n",
    "redSources = list(dfTable_rep['red'])\n",
    "\n",
    "# Calculate the positions for the bars for both layers\n",
    "x = np.arange(len(names))\n",
    "\n",
    "# Create the first layer of bars\n",
    "plt.bar(x, blueSources, label= 'Liberal Media Sources') #  color = 'blue'\n",
    "# Create the second layer of bars on top of the first layer\n",
    "plt.bar(x, redSources, bottom= blueSources, label='Conservative Media Sources', color = 'red')\n",
    "\n",
    "# Add labels \n",
    "plt.xlabel('')\n",
    "plt.ylabel('Frequency of News Citations', fontsize = 14)\n",
    "# 0-334 reprsent democrats; 334-394 senator democrats; 394 -803; the rest senator republican \n",
    "ax.axvline(x=405, linestyle='--', color='k')\n",
    "#ax.axvline(x=390, linestyle='--', color='k')\n",
    "#ax.axvline(x=795, linestyle='--', color='k')\n",
    "\n",
    "# add a legend\n",
    "plt.legend(fontsize = 14)\n",
    "# remove xticks \n",
    "plt.xticks([])\n",
    "plt.yticks(np.arange(0,360,50))\n",
    "#remove spines\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "# Show the plot or save it to a file\n",
    "#plt.savefig('.../frequencyCitations_rep.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "738e7ac7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f06d1bd6",
   "metadata": {},
   "source": [
    "## one sample t test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "91c3df8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T21:43:13.125822Z",
     "start_time": "2023-12-27T21:43:13.106110Z"
    }
   },
   "outputs": [],
   "source": [
    "dfRegre = pd.read_csv('.../regressionTableUpdated2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b59ad893",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T21:43:14.032849Z",
     "start_time": "2023-12-27T21:43:14.004898Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>newNames</th>\n",
       "      <th>biasWithoutSum</th>\n",
       "      <th>biasWithoutSum_zscore</th>\n",
       "      <th>partyDummy</th>\n",
       "      <th>genderDummy</th>\n",
       "      <th>jobDummy</th>\n",
       "      <th>age_zscore</th>\n",
       "      <th>monthlyEdit_zscore</th>\n",
       "      <th>pageviews</th>\n",
       "      <th>editors</th>\n",
       "      <th>...</th>\n",
       "      <th>outLink_zscore</th>\n",
       "      <th>inLink_zscore</th>\n",
       "      <th>unique_references_zscore</th>\n",
       "      <th>words_zscore</th>\n",
       "      <th>characters_zscore</th>\n",
       "      <th>editors_zscore</th>\n",
       "      <th>pageviews_zscore</th>\n",
       "      <th>gini_index</th>\n",
       "      <th>gini_zscore</th>\n",
       "      <th>wordHuman_zscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chuck_Grassley</td>\n",
       "      <td>-0.442177</td>\n",
       "      <td>0.292297</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.270586</td>\n",
       "      <td>0.576590</td>\n",
       "      <td>41643</td>\n",
       "      <td>939</td>\n",
       "      <td>...</td>\n",
       "      <td>1.487689</td>\n",
       "      <td>1.471997</td>\n",
       "      <td>1.591714</td>\n",
       "      <td>1.702650</td>\n",
       "      <td>1.760030</td>\n",
       "      <td>1.576381</td>\n",
       "      <td>1.275870</td>\n",
       "      <td>0.440365</td>\n",
       "      <td>-0.346172</td>\n",
       "      <td>-0.315371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mitch_McConnell</td>\n",
       "      <td>-0.637363</td>\n",
       "      <td>-0.286548</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.560672</td>\n",
       "      <td>2.928292</td>\n",
       "      <td>49461</td>\n",
       "      <td>1937</td>\n",
       "      <td>...</td>\n",
       "      <td>2.506149</td>\n",
       "      <td>4.148224</td>\n",
       "      <td>2.433294</td>\n",
       "      <td>2.076332</td>\n",
       "      <td>2.149130</td>\n",
       "      <td>4.312472</td>\n",
       "      <td>1.589072</td>\n",
       "      <td>0.563755</td>\n",
       "      <td>1.359692</td>\n",
       "      <td>1.050987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dianne_Feinstein</td>\n",
       "      <td>-0.592920</td>\n",
       "      <td>-0.154750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.270586</td>\n",
       "      <td>0.937662</td>\n",
       "      <td>65884</td>\n",
       "      <td>1942</td>\n",
       "      <td>...</td>\n",
       "      <td>2.154956</td>\n",
       "      <td>2.759522</td>\n",
       "      <td>2.027532</td>\n",
       "      <td>1.653865</td>\n",
       "      <td>1.708176</td>\n",
       "      <td>4.326180</td>\n",
       "      <td>2.247004</td>\n",
       "      <td>0.508476</td>\n",
       "      <td>0.595465</td>\n",
       "      <td>0.786341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Patty_Murray</td>\n",
       "      <td>-0.814815</td>\n",
       "      <td>-0.812802</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.929637</td>\n",
       "      <td>-0.103632</td>\n",
       "      <td>19482</td>\n",
       "      <td>716</td>\n",
       "      <td>...</td>\n",
       "      <td>0.406015</td>\n",
       "      <td>1.099031</td>\n",
       "      <td>0.118950</td>\n",
       "      <td>0.554948</td>\n",
       "      <td>0.562194</td>\n",
       "      <td>0.965009</td>\n",
       "      <td>0.388064</td>\n",
       "      <td>0.422880</td>\n",
       "      <td>-0.587901</td>\n",
       "      <td>-0.159746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ron_Wyden</td>\n",
       "      <td>-0.803030</td>\n",
       "      <td>-0.777854</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.008516</td>\n",
       "      <td>-0.140145</td>\n",
       "      <td>9819</td>\n",
       "      <td>655</td>\n",
       "      <td>...</td>\n",
       "      <td>1.115425</td>\n",
       "      <td>0.970080</td>\n",
       "      <td>1.396348</td>\n",
       "      <td>2.540543</td>\n",
       "      <td>2.540073</td>\n",
       "      <td>0.797773</td>\n",
       "      <td>0.000949</td>\n",
       "      <td>0.434123</td>\n",
       "      <td>-0.432461</td>\n",
       "      <td>-0.114248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           newNames  biasWithoutSum  biasWithoutSum_zscore  partyDummy  \\\n",
       "0    Chuck_Grassley       -0.442177               0.292297           1   \n",
       "1   Mitch_McConnell       -0.637363              -0.286548           1   \n",
       "2  Dianne_Feinstein       -0.592920              -0.154750           0   \n",
       "3      Patty_Murray       -0.814815              -0.812802           0   \n",
       "4         Ron_Wyden       -0.803030              -0.777854           0   \n",
       "\n",
       "   genderDummy  jobDummy  age_zscore  monthlyEdit_zscore  pageviews  editors  \\\n",
       "0            1         1    2.270586            0.576590      41643      939   \n",
       "1            1         1    1.560672            2.928292      49461     1937   \n",
       "2            0         1    2.270586            0.937662      65884     1942   \n",
       "3            0         1    0.929637           -0.103632      19482      716   \n",
       "4            1         1    1.008516           -0.140145       9819      655   \n",
       "\n",
       "   ... outLink_zscore  inLink_zscore  unique_references_zscore  words_zscore  \\\n",
       "0  ...       1.487689       1.471997                  1.591714      1.702650   \n",
       "1  ...       2.506149       4.148224                  2.433294      2.076332   \n",
       "2  ...       2.154956       2.759522                  2.027532      1.653865   \n",
       "3  ...       0.406015       1.099031                  0.118950      0.554948   \n",
       "4  ...       1.115425       0.970080                  1.396348      2.540543   \n",
       "\n",
       "   characters_zscore  editors_zscore  pageviews_zscore  gini_index  \\\n",
       "0           1.760030        1.576381          1.275870    0.440365   \n",
       "1           2.149130        4.312472          1.589072    0.563755   \n",
       "2           1.708176        4.326180          2.247004    0.508476   \n",
       "3           0.562194        0.965009          0.388064    0.422880   \n",
       "4           2.540073        0.797773          0.000949    0.434123   \n",
       "\n",
       "   gini_zscore  wordHuman_zscore  \n",
       "0    -0.346172         -0.315371  \n",
       "1     1.359692          1.050987  \n",
       "2     0.595465          0.786341  \n",
       "3    -0.587901         -0.159746  \n",
       "4    -0.432461         -0.114248  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfRegre.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0067627b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T21:43:22.783920Z",
     "start_time": "2023-12-27T21:43:22.772409Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "872"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# three independents are removed. \n",
    "len(dfRegre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e6887c20",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-26T19:04:44.393254Z",
     "start_time": "2023-12-26T19:04:44.372507Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>names</th>\n",
       "      <th>urlTail</th>\n",
       "      <th>party</th>\n",
       "      <th>states</th>\n",
       "      <th>jobTitles</th>\n",
       "      <th>newNames</th>\n",
       "      <th>urlRefer</th>\n",
       "      <th>namesTalkPage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Chuck Grassley</td>\n",
       "      <td>/wiki/Chuck_Grassley</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>senator</td>\n",
       "      <td>Chuck_Grassley</td>\n",
       "      <td>https://en.wikipedia.org/w/index.php?title=Chu...</td>\n",
       "      <td>Chuck_Grassley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Mitch McConnell</td>\n",
       "      <td>/wiki/Mitch_McConnell</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>senator</td>\n",
       "      <td>Mitch_McConnell</td>\n",
       "      <td>https://en.wikipedia.org/w/index.php?title=Mit...</td>\n",
       "      <td>Mitch_McConnell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Dianne Feinstein</td>\n",
       "      <td>/wiki/Dianne_Feinstein</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>California</td>\n",
       "      <td>senator</td>\n",
       "      <td>Dianne_Feinstein</td>\n",
       "      <td>https://en.wikipedia.org/w/index.php?title=Dia...</td>\n",
       "      <td>Dianne_Feinstein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Patty Murray</td>\n",
       "      <td>/wiki/Patty_Murray</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Washington</td>\n",
       "      <td>senator</td>\n",
       "      <td>Patty_Murray</td>\n",
       "      <td>https://en.wikipedia.org/w/index.php?title=Pat...</td>\n",
       "      <td>Patty_Murray</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Ron Wyden</td>\n",
       "      <td>/wiki/Ron_Wyden</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>senator</td>\n",
       "      <td>Ron_Wyden</td>\n",
       "      <td>https://en.wikipedia.org/w/index.php?title=Ron...</td>\n",
       "      <td>Ron_Wyden</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0             names                 urlTail       party  \\\n",
       "0           0    Chuck Grassley    /wiki/Chuck_Grassley  Republican   \n",
       "1           1   Mitch McConnell   /wiki/Mitch_McConnell  Republican   \n",
       "2           2  Dianne Feinstein  /wiki/Dianne_Feinstein  Democratic   \n",
       "3           3      Patty Murray      /wiki/Patty_Murray  Democratic   \n",
       "4           4         Ron Wyden         /wiki/Ron_Wyden  Democratic   \n",
       "\n",
       "       states jobTitles          newNames  \\\n",
       "0        Iowa   senator    Chuck_Grassley   \n",
       "1    Kentucky   senator   Mitch_McConnell   \n",
       "2  California   senator  Dianne_Feinstein   \n",
       "3  Washington   senator      Patty_Murray   \n",
       "4      Oregon   senator         Ron_Wyden   \n",
       "\n",
       "                                            urlRefer     namesTalkPage  \n",
       "0  https://en.wikipedia.org/w/index.php?title=Chu...    Chuck_Grassley  \n",
       "1  https://en.wikipedia.org/w/index.php?title=Mit...   Mitch_McConnell  \n",
       "2  https://en.wikipedia.org/w/index.php?title=Dia...  Dianne_Feinstein  \n",
       "3  https://en.wikipedia.org/w/index.php?title=Pat...      Patty_Murray  \n",
       "4  https://en.wikipedia.org/w/index.php?title=Ron...         Ron_Wyden  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfMembers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e6972fa6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T21:47:26.165783Z",
     "start_time": "2023-12-27T21:47:26.102208Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I checked that the regression table doesn't contain mulitple pages of the same politicisn\n",
    "[i for i in list(dfRegre['newNames']) if i not in list(dfMembers['newNames'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e55f85e0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T21:48:42.756744Z",
     "start_time": "2023-12-27T21:48:42.691569Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Bernie_Sanders', 'Angus_King', 'Kyrsten_Sinema']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I checked that the regression table doesn't contain mulitple pages of the same politicisn\n",
    "[i for i in list(dfMembers['newNames']) if i not in list(dfRegre['newNames'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "664873aa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T21:49:31.588868Z",
     "start_time": "2023-12-27T21:49:31.571824Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>names</th>\n",
       "      <th>urlTail</th>\n",
       "      <th>party</th>\n",
       "      <th>states</th>\n",
       "      <th>jobTitles</th>\n",
       "      <th>newNames</th>\n",
       "      <th>urlRefer</th>\n",
       "      <th>namesTalkPage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>Angus King</td>\n",
       "      <td>/wiki/Angus_King</td>\n",
       "      <td>Independent</td>\n",
       "      <td>Maine</td>\n",
       "      <td>senator</td>\n",
       "      <td>Angus_King</td>\n",
       "      <td>https://en.wikipedia.org/w/index.php?title=Ang...</td>\n",
       "      <td>Angus_King</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0       names           urlTail        party states jobTitles  \\\n",
       "49          49  Angus King  /wiki/Angus_King  Independent  Maine   senator   \n",
       "\n",
       "      newNames                                           urlRefer  \\\n",
       "49  Angus_King  https://en.wikipedia.org/w/index.php?title=Ang...   \n",
       "\n",
       "   namesTalkPage  \n",
       "49    Angus_King  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfMembers[dfMembers['newNames'] == 'Angus_King']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5b72785c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-01T21:12:49.137810Z",
     "start_time": "2023-10-01T21:12:49.129743Z"
    }
   },
   "outputs": [],
   "source": [
    "#dfRegre['biasWithoutSum'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "06d88f5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-01T21:15:28.722356Z",
     "start_time": "2023-10-01T21:15:28.712203Z"
    }
   },
   "outputs": [],
   "source": [
    "biasList = list(dfRegre['biasWithoutSum'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ead9d8ac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-01T23:58:23.380744Z",
     "start_time": "2023-10-01T23:58:23.370007Z"
    }
   },
   "outputs": [],
   "source": [
    "mean = ss.mean(biasList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "67666947",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-01T23:58:54.360548Z",
     "start_time": "2023-10-01T23:58:54.345932Z"
    }
   },
   "outputs": [],
   "source": [
    "std = ss.stdev(biasList) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a1e72922",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-01T23:59:03.055796Z",
     "start_time": "2023-10-01T23:59:03.042337Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.5399372669484336 0.33741956987666294\n"
     ]
    }
   ],
   "source": [
    "print(mean, std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dacd82e3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-02T00:07:11.916276Z",
     "start_time": "2023-10-02T00:07:11.901624Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "872"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(biasList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "52b00b41",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-01T21:15:29.671039Z",
     "start_time": "2023-10-01T21:15:29.660350Z"
    }
   },
   "outputs": [],
   "source": [
    "population_mean = 0\n",
    "t_statistic, p_value = stats.ttest_1samp(biasList, population_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6a30d286",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-01T21:15:30.177016Z",
     "start_time": "2023-10-01T21:15:30.170762Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-47.25320593013419 1.4365830890468737e-242\n"
     ]
    }
   ],
   "source": [
    "print(t_statistic, p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1aa7bc04",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "11995460",
   "metadata": {},
   "source": [
    "## regressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a71ccc90",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-29T19:15:30.787204Z",
     "start_time": "2023-09-29T19:15:30.774096Z"
    }
   },
   "outputs": [],
   "source": [
    "dfRegre.drop('wordHuman_zscore', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "302a9479",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-29T19:15:31.306566Z",
     "start_time": "2023-09-29T19:15:31.281052Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>newNames</th>\n",
       "      <th>biasWithoutSum</th>\n",
       "      <th>biasWithoutSum_zscore</th>\n",
       "      <th>partyDummy</th>\n",
       "      <th>genderDummy</th>\n",
       "      <th>jobDummy</th>\n",
       "      <th>age_zscore</th>\n",
       "      <th>monthlyEdit_zscore</th>\n",
       "      <th>pageviews</th>\n",
       "      <th>editors</th>\n",
       "      <th>...</th>\n",
       "      <th>assessValueDummy</th>\n",
       "      <th>outLink_zscore</th>\n",
       "      <th>inLink_zscore</th>\n",
       "      <th>unique_references_zscore</th>\n",
       "      <th>words_zscore</th>\n",
       "      <th>characters_zscore</th>\n",
       "      <th>editors_zscore</th>\n",
       "      <th>pageviews_zscore</th>\n",
       "      <th>gini_index</th>\n",
       "      <th>gini_zscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chuck_Grassley</td>\n",
       "      <td>-0.442177</td>\n",
       "      <td>0.292297</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.270586</td>\n",
       "      <td>0.576590</td>\n",
       "      <td>41643</td>\n",
       "      <td>939</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1.487689</td>\n",
       "      <td>1.471997</td>\n",
       "      <td>1.591714</td>\n",
       "      <td>1.702650</td>\n",
       "      <td>1.760030</td>\n",
       "      <td>1.576381</td>\n",
       "      <td>1.275870</td>\n",
       "      <td>0.440365</td>\n",
       "      <td>-0.346172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mitch_McConnell</td>\n",
       "      <td>-0.637363</td>\n",
       "      <td>-0.286548</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.560672</td>\n",
       "      <td>2.928292</td>\n",
       "      <td>49461</td>\n",
       "      <td>1937</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2.506149</td>\n",
       "      <td>4.148224</td>\n",
       "      <td>2.433294</td>\n",
       "      <td>2.076332</td>\n",
       "      <td>2.149130</td>\n",
       "      <td>4.312472</td>\n",
       "      <td>1.589072</td>\n",
       "      <td>0.563755</td>\n",
       "      <td>1.359692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dianne_Feinstein</td>\n",
       "      <td>-0.592920</td>\n",
       "      <td>-0.154750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.270586</td>\n",
       "      <td>0.937662</td>\n",
       "      <td>65884</td>\n",
       "      <td>1942</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2.154956</td>\n",
       "      <td>2.759522</td>\n",
       "      <td>2.027532</td>\n",
       "      <td>1.653865</td>\n",
       "      <td>1.708176</td>\n",
       "      <td>4.326180</td>\n",
       "      <td>2.247004</td>\n",
       "      <td>0.508476</td>\n",
       "      <td>0.595465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Patty_Murray</td>\n",
       "      <td>-0.814815</td>\n",
       "      <td>-0.812802</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.929637</td>\n",
       "      <td>-0.103632</td>\n",
       "      <td>19482</td>\n",
       "      <td>716</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.406015</td>\n",
       "      <td>1.099031</td>\n",
       "      <td>0.118950</td>\n",
       "      <td>0.554948</td>\n",
       "      <td>0.562194</td>\n",
       "      <td>0.965009</td>\n",
       "      <td>0.388064</td>\n",
       "      <td>0.422880</td>\n",
       "      <td>-0.587901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ron_Wyden</td>\n",
       "      <td>-0.803030</td>\n",
       "      <td>-0.777854</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.008516</td>\n",
       "      <td>-0.140145</td>\n",
       "      <td>9819</td>\n",
       "      <td>655</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1.115425</td>\n",
       "      <td>0.970080</td>\n",
       "      <td>1.396348</td>\n",
       "      <td>2.540543</td>\n",
       "      <td>2.540073</td>\n",
       "      <td>0.797773</td>\n",
       "      <td>0.000949</td>\n",
       "      <td>0.434123</td>\n",
       "      <td>-0.432461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           newNames  biasWithoutSum  biasWithoutSum_zscore  partyDummy  \\\n",
       "0    Chuck_Grassley       -0.442177               0.292297           1   \n",
       "1   Mitch_McConnell       -0.637363              -0.286548           1   \n",
       "2  Dianne_Feinstein       -0.592920              -0.154750           0   \n",
       "3      Patty_Murray       -0.814815              -0.812802           0   \n",
       "4         Ron_Wyden       -0.803030              -0.777854           0   \n",
       "\n",
       "   genderDummy  jobDummy  age_zscore  monthlyEdit_zscore  pageviews  editors  \\\n",
       "0            1         1    2.270586            0.576590      41643      939   \n",
       "1            1         1    1.560672            2.928292      49461     1937   \n",
       "2            0         1    2.270586            0.937662      65884     1942   \n",
       "3            0         1    0.929637           -0.103632      19482      716   \n",
       "4            1         1    1.008516           -0.140145       9819      655   \n",
       "\n",
       "   ... assessValueDummy  outLink_zscore  inLink_zscore  \\\n",
       "0  ...                0        1.487689       1.471997   \n",
       "1  ...                1        2.506149       4.148224   \n",
       "2  ...                1        2.154956       2.759522   \n",
       "3  ...                0        0.406015       1.099031   \n",
       "4  ...                1        1.115425       0.970080   \n",
       "\n",
       "   unique_references_zscore  words_zscore  characters_zscore  editors_zscore  \\\n",
       "0                  1.591714      1.702650           1.760030        1.576381   \n",
       "1                  2.433294      2.076332           2.149130        4.312472   \n",
       "2                  2.027532      1.653865           1.708176        4.326180   \n",
       "3                  0.118950      0.554948           0.562194        0.965009   \n",
       "4                  1.396348      2.540543           2.540073        0.797773   \n",
       "\n",
       "   pageviews_zscore  gini_index  gini_zscore  \n",
       "0          1.275870    0.440365    -0.346172  \n",
       "1          1.589072    0.563755     1.359692  \n",
       "2          2.247004    0.508476     0.595465  \n",
       "3          0.388064    0.422880    -0.587901  \n",
       "4          0.000949    0.434123    -0.432461  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfRegre.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f66eeff2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-29T19:15:32.456327Z",
     "start_time": "2023-09-29T19:15:32.441155Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['newNames', 'biasWithoutSum', 'biasWithoutSum_zscore', 'partyDummy',\n",
       "       'genderDummy', 'jobDummy', 'age_zscore', 'monthlyEdit_zscore',\n",
       "       'pageviews', 'editors', 'assessValue', 'characters', 'words',\n",
       "       'unique_references', 'links_out_count', 'links_in_count',\n",
       "       'assessValueDummy', 'outLink_zscore', 'inLink_zscore',\n",
       "       'unique_references_zscore', 'words_zscore', 'characters_zscore',\n",
       "       'editors_zscore', 'pageviews_zscore', 'gini_index', 'gini_zscore'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfRegre.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a89e6672",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-29T19:15:34.968072Z",
     "start_time": "2023-09-29T19:15:34.947855Z"
    }
   },
   "outputs": [],
   "source": [
    "dfTalkPage2 = pd.read_csv('.../talkPageMetricUpdated.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6b4d16c2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-29T19:15:36.035925Z",
     "start_time": "2023-09-29T19:15:36.025092Z"
    }
   },
   "outputs": [],
   "source": [
    "dfTalkPage3 = dfTalkPage2[['newNames','namesTalkPage','wordHuman_zscore','wordHuBot_zscore',\n",
    "                          'letHuman_zscore', 'letHuBot_zscore', 'editorCount_zscore', 'editorCountHu_zscore',\n",
    "                          'letterCount_zscore', 'letNumCount_zscore', 'wordNumCount_zscore', 'wordCount_zscore',\n",
    "                          'editCount_zscore', 'editCountHu_zscore']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4086ed8f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-29T19:15:37.822717Z",
     "start_time": "2023-09-29T19:15:37.808795Z"
    }
   },
   "outputs": [],
   "source": [
    "dfRegre2 = pd.merge(dfRegre, dfTalkPage3, on = 'newNames')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f9649551",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-29T19:15:38.467616Z",
     "start_time": "2023-09-29T19:15:38.442572Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>newNames</th>\n",
       "      <th>biasWithoutSum</th>\n",
       "      <th>biasWithoutSum_zscore</th>\n",
       "      <th>partyDummy</th>\n",
       "      <th>genderDummy</th>\n",
       "      <th>jobDummy</th>\n",
       "      <th>age_zscore</th>\n",
       "      <th>monthlyEdit_zscore</th>\n",
       "      <th>pageviews</th>\n",
       "      <th>editors</th>\n",
       "      <th>...</th>\n",
       "      <th>letHuman_zscore</th>\n",
       "      <th>letHuBot_zscore</th>\n",
       "      <th>editorCount_zscore</th>\n",
       "      <th>editorCountHu_zscore</th>\n",
       "      <th>letterCount_zscore</th>\n",
       "      <th>letNumCount_zscore</th>\n",
       "      <th>wordNumCount_zscore</th>\n",
       "      <th>wordCount_zscore</th>\n",
       "      <th>editCount_zscore</th>\n",
       "      <th>editCountHu_zscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chuck_Grassley</td>\n",
       "      <td>-0.442177</td>\n",
       "      <td>0.292297</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.270586</td>\n",
       "      <td>0.576590</td>\n",
       "      <td>41643</td>\n",
       "      <td>939</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.306705</td>\n",
       "      <td>-0.257957</td>\n",
       "      <td>0.954943</td>\n",
       "      <td>0.953339</td>\n",
       "      <td>0.253704</td>\n",
       "      <td>0.260843</td>\n",
       "      <td>0.252277</td>\n",
       "      <td>0.242173</td>\n",
       "      <td>0.288408</td>\n",
       "      <td>0.278914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mitch_McConnell</td>\n",
       "      <td>-0.637363</td>\n",
       "      <td>-0.286548</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.560672</td>\n",
       "      <td>2.928292</td>\n",
       "      <td>49461</td>\n",
       "      <td>1937</td>\n",
       "      <td>...</td>\n",
       "      <td>0.128567</td>\n",
       "      <td>0.091552</td>\n",
       "      <td>2.418913</td>\n",
       "      <td>2.401592</td>\n",
       "      <td>1.651702</td>\n",
       "      <td>1.648654</td>\n",
       "      <td>1.595855</td>\n",
       "      <td>1.602110</td>\n",
       "      <td>1.166569</td>\n",
       "      <td>0.997998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dianne_Feinstein</td>\n",
       "      <td>-0.592920</td>\n",
       "      <td>-0.154750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.270586</td>\n",
       "      <td>0.937662</td>\n",
       "      <td>65884</td>\n",
       "      <td>1942</td>\n",
       "      <td>...</td>\n",
       "      <td>0.698165</td>\n",
       "      <td>0.798954</td>\n",
       "      <td>2.044665</td>\n",
       "      <td>2.033782</td>\n",
       "      <td>1.620953</td>\n",
       "      <td>1.620428</td>\n",
       "      <td>1.595622</td>\n",
       "      <td>1.599487</td>\n",
       "      <td>0.672494</td>\n",
       "      <td>0.615109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Patty_Murray</td>\n",
       "      <td>-0.814815</td>\n",
       "      <td>-0.812802</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.929637</td>\n",
       "      <td>-0.103632</td>\n",
       "      <td>19482</td>\n",
       "      <td>716</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.174975</td>\n",
       "      <td>-0.106084</td>\n",
       "      <td>0.096374</td>\n",
       "      <td>0.091283</td>\n",
       "      <td>-0.021156</td>\n",
       "      <td>-0.022174</td>\n",
       "      <td>-0.018398</td>\n",
       "      <td>-0.016794</td>\n",
       "      <td>-0.022353</td>\n",
       "      <td>-0.021794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ron_Wyden</td>\n",
       "      <td>-0.803030</td>\n",
       "      <td>-0.777854</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.008516</td>\n",
       "      <td>-0.140145</td>\n",
       "      <td>9819</td>\n",
       "      <td>655</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.172582</td>\n",
       "      <td>-0.148138</td>\n",
       "      <td>0.140403</td>\n",
       "      <td>0.125765</td>\n",
       "      <td>-0.084176</td>\n",
       "      <td>-0.084844</td>\n",
       "      <td>-0.089801</td>\n",
       "      <td>-0.089286</td>\n",
       "      <td>-0.069491</td>\n",
       "      <td>-0.074091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           newNames  biasWithoutSum  biasWithoutSum_zscore  partyDummy  \\\n",
       "0    Chuck_Grassley       -0.442177               0.292297           1   \n",
       "1   Mitch_McConnell       -0.637363              -0.286548           1   \n",
       "2  Dianne_Feinstein       -0.592920              -0.154750           0   \n",
       "3      Patty_Murray       -0.814815              -0.812802           0   \n",
       "4         Ron_Wyden       -0.803030              -0.777854           0   \n",
       "\n",
       "   genderDummy  jobDummy  age_zscore  monthlyEdit_zscore  pageviews  editors  \\\n",
       "0            1         1    2.270586            0.576590      41643      939   \n",
       "1            1         1    1.560672            2.928292      49461     1937   \n",
       "2            0         1    2.270586            0.937662      65884     1942   \n",
       "3            0         1    0.929637           -0.103632      19482      716   \n",
       "4            1         1    1.008516           -0.140145       9819      655   \n",
       "\n",
       "   ... letHuman_zscore  letHuBot_zscore  editorCount_zscore  \\\n",
       "0  ...       -0.306705        -0.257957            0.954943   \n",
       "1  ...        0.128567         0.091552            2.418913   \n",
       "2  ...        0.698165         0.798954            2.044665   \n",
       "3  ...       -0.174975        -0.106084            0.096374   \n",
       "4  ...       -0.172582        -0.148138            0.140403   \n",
       "\n",
       "   editorCountHu_zscore  letterCount_zscore  letNumCount_zscore  \\\n",
       "0              0.953339            0.253704            0.260843   \n",
       "1              2.401592            1.651702            1.648654   \n",
       "2              2.033782            1.620953            1.620428   \n",
       "3              0.091283           -0.021156           -0.022174   \n",
       "4              0.125765           -0.084176           -0.084844   \n",
       "\n",
       "   wordNumCount_zscore  wordCount_zscore  editCount_zscore  editCountHu_zscore  \n",
       "0             0.252277          0.242173          0.288408            0.278914  \n",
       "1             1.595855          1.602110          1.166569            0.997998  \n",
       "2             1.595622          1.599487          0.672494            0.615109  \n",
       "3            -0.018398         -0.016794         -0.022353           -0.021794  \n",
       "4            -0.089801         -0.089286         -0.069491           -0.074091  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfRegre2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b49e7369",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-29T19:15:48.819464Z",
     "start_time": "2023-09-29T19:15:48.807804Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['newNames', 'biasWithoutSum', 'biasWithoutSum_zscore', 'partyDummy',\n",
       "       'genderDummy', 'jobDummy', 'age_zscore', 'monthlyEdit_zscore',\n",
       "       'pageviews', 'editors', 'assessValue', 'characters', 'words',\n",
       "       'unique_references', 'links_out_count', 'links_in_count',\n",
       "       'assessValueDummy', 'outLink_zscore', 'inLink_zscore',\n",
       "       'unique_references_zscore', 'words_zscore', 'characters_zscore',\n",
       "       'editors_zscore', 'pageviews_zscore', 'gini_index', 'gini_zscore',\n",
       "       'namesTalkPage', 'wordHuman_zscore', 'wordHuBot_zscore',\n",
       "       'letHuman_zscore', 'letHuBot_zscore', 'editorCount_zscore',\n",
       "       'editorCountHu_zscore', 'letterCount_zscore', 'letNumCount_zscore',\n",
       "       'wordNumCount_zscore', 'wordCount_zscore', 'editCount_zscore',\n",
       "       'editCountHu_zscore'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfRegre2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1cd7454a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-29T19:15:50.225037Z",
     "start_time": "2023-09-29T19:15:50.216581Z"
    }
   },
   "outputs": [],
   "source": [
    "# the range of work diversity is [0,1]\n",
    "# as the value grows, the inequality of workload distribution increases\n",
    "\n",
    "# the range of work diversity is [-1, 0]\n",
    "# as the value grows, the diversity of workload increases. (the inequality of workload distribution decreases)\n",
    "\n",
    "# change the direction of work diversity \n",
    "giniList = [i * -1 for i in list(dfRegre2['gini_index'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "04f88b43",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-29T19:15:51.458601Z",
     "start_time": "2023-09-29T19:15:51.446211Z"
    }
   },
   "outputs": [],
   "source": [
    "# the differences with gini zscore due to the number of obervations \n",
    "# while calculating gini zscore, there were 875 units. But there are 872 in dfRegre\n",
    "dfRegre2['gini_zscore_reverse'] = stats.zscore(giniList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3991ae10",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-29T19:15:52.716230Z",
     "start_time": "2023-09-29T19:15:52.706139Z"
    }
   },
   "outputs": [],
   "source": [
    "# specify the formula   DV ~ IV     the interaction term is not significant  main effects yes!\n",
    "reg = \"biasWithoutSum ~ monthlyEdit_zscore + wordHuman_zscore + gini_zscore_reverse + editors_zscore + C(assessValueDummy) + age_zscore  + unique_references_zscore + characters_zscore + pageviews_zscore + C(jobDummy) + C(partyDummy) + C(genderDummy)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9cac7407",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-29T19:15:54.353211Z",
     "start_time": "2023-09-29T19:15:54.294003Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n"
     ]
    }
   ],
   "source": [
    "# fit the model\n",
    "regOutput = smf.ols(reg, dfRegre2).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3bcd90c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# workload diversity :  gini_zscore_reverse\n",
    "# communication intensity: wordHuman_zscore\n",
    "# editors_zscore:  crowd size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8be4cbcf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-29T19:15:58.584646Z",
     "start_time": "2023-09-29T19:15:58.554708Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         biasWithoutSum   R-squared:                       0.100\n",
      "Model:                            OLS   Adj. R-squared:                  0.088\n",
      "Method:                 Least Squares   F-statistic:                     7.989\n",
      "Date:                Fri, 29 Sep 2023   Prob (F-statistic):           2.63e-14\n",
      "Time:                        12:15:58   Log-Likelihood:                -243.32\n",
      "No. Observations:                 872   AIC:                             512.6\n",
      "Df Residuals:                     859   BIC:                             574.7\n",
      "Df Model:                          12                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "============================================================================================\n",
      "                               coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------------\n",
      "Intercept                   -0.6041      0.026    -23.685      0.000      -0.654      -0.554\n",
      "C(assessValueDummy)[T.1]     0.0041      0.032      0.128      0.898      -0.059       0.068\n",
      "C(jobDummy)[T.1]             0.0793      0.035      2.234      0.026       0.010       0.149\n",
      "C(partyDummy)[T.1]           0.1181      0.023      5.048      0.000       0.072       0.164\n",
      "C(genderDummy)[T.1]         -0.0190      0.027     -0.693      0.489      -0.073       0.035\n",
      "monthlyEdit_zscore          -0.0402      0.016     -2.500      0.013      -0.072      -0.009\n",
      "wordHuman_zscore            -0.0269      0.011     -2.418      0.016      -0.049      -0.005\n",
      "gini_zscore_reverse         -0.0420      0.014     -3.044      0.002      -0.069      -0.015\n",
      "editors_zscore              -0.0241      0.021     -1.147      0.252      -0.065       0.017\n",
      "age_zscore                  -0.0487      0.013     -3.647      0.000      -0.075      -0.022\n",
      "unique_references_zscore     0.0266      0.037      0.721      0.471      -0.046       0.099\n",
      "characters_zscore           -0.0423      0.033     -1.279      0.201      -0.107       0.023\n",
      "pageviews_zscore             0.0159      0.014      1.098      0.273      -0.013       0.044\n",
      "==============================================================================\n",
      "Omnibus:                      193.056   Durbin-Watson:                   1.923\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              416.293\n",
      "Skew:                           1.220   Prob(JB):                     4.01e-91\n",
      "Kurtosis:                       5.345   Cond. No.                         8.55\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(regOutput.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c7b0d37",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7271b503",
   "metadata": {},
   "source": [
    "## plot coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ba93cdbd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-02T17:01:40.651873Z",
     "start_time": "2023-10-02T17:01:40.611116Z"
    }
   },
   "outputs": [],
   "source": [
    "dfRegre2 = pd.read_csv('.../regressionTableUpdated3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cbbfb616",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-02T17:01:42.089987Z",
     "start_time": "2023-10-02T17:01:42.078978Z"
    }
   },
   "outputs": [],
   "source": [
    "# the range of gini index is [0,1]\n",
    "# as the value grows, the inequality of wealth increases\n",
    "\n",
    "# the range of work diversity is [0,1]\n",
    "# as the value grows, the inequality of workload distribution increases\n",
    "\n",
    "# the range of work diversity is [-1, 0]\n",
    "# as the value grows, the diversity of workload increases. (the inequality of workload distribution decreases)\n",
    "\n",
    "# change the direction of work diversity \n",
    "giniList = [i * -1 for i in list(dfRegre2['gini_index'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6abad535",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-02T17:01:43.141634Z",
     "start_time": "2023-10-02T17:01:43.128324Z"
    }
   },
   "outputs": [],
   "source": [
    "# the differences with gini zscore due to the number of obervations \n",
    "# while calculating gini zscore, there were 875 units. But there are 872 in dfRegre\n",
    "dfRegre2['gini_zscore_reverse'] = stats.zscore(giniList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "39009b1c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-02T17:01:44.010844Z",
     "start_time": "2023-10-02T17:01:44.003231Z"
    }
   },
   "outputs": [],
   "source": [
    "# specify the formula   DV ~ IV     the interaction term is not significant  main effects yes!\n",
    "reg = \"biasWithoutSum ~ monthlyEdit_zscore + wordHuman_zscore + gini_zscore_reverse + editors_zscore + C(assessValueDummy) + age_zscore  + unique_references_zscore + characters_zscore + pageviews_zscore + C(jobDummy) + C(partyDummy) + C(genderDummy)\"\n",
    "# 'wordHuBot_zscore' wordHuman_zscore +  the effect of words/edits on bias\n",
    "# is significant with or without bots \n",
    "#  editorCountHu_zscore  editorCount_zscore not significant\n",
    "# letterCount_zscore letNumCount_zscore wordCount_zscore wordNumCount_zscore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "383fa3e8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-02T17:01:45.078368Z",
     "start_time": "2023-10-02T17:01:45.024649Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n"
     ]
    }
   ],
   "source": [
    "# fit the model\n",
    "regOutput = smf.ols(reg, dfRegre2).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7b46a106",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-02T00:37:04.950112Z",
     "start_time": "2023-10-02T00:37:04.944029Z"
    }
   },
   "outputs": [],
   "source": [
    "#dfRegre2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fe19c73f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-02T17:01:47.184303Z",
     "start_time": "2023-10-02T17:01:47.151413Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         biasWithoutSum   R-squared:                       0.100\n",
      "Model:                            OLS   Adj. R-squared:                  0.088\n",
      "Method:                 Least Squares   F-statistic:                     7.989\n",
      "Date:                Mon, 02 Oct 2023   Prob (F-statistic):           2.63e-14\n",
      "Time:                        10:01:47   Log-Likelihood:                -243.32\n",
      "No. Observations:                 872   AIC:                             512.6\n",
      "Df Residuals:                     859   BIC:                             574.7\n",
      "Df Model:                          12                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "============================================================================================\n",
      "                               coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------------\n",
      "Intercept                   -0.6041      0.026    -23.685      0.000      -0.654      -0.554\n",
      "C(assessValueDummy)[T.1]     0.0041      0.032      0.128      0.898      -0.059       0.068\n",
      "C(jobDummy)[T.1]             0.0793      0.035      2.234      0.026       0.010       0.149\n",
      "C(partyDummy)[T.1]           0.1181      0.023      5.048      0.000       0.072       0.164\n",
      "C(genderDummy)[T.1]         -0.0190      0.027     -0.693      0.489      -0.073       0.035\n",
      "monthlyEdit_zscore          -0.0402      0.016     -2.500      0.013      -0.072      -0.009\n",
      "wordHuman_zscore            -0.0269      0.011     -2.418      0.016      -0.049      -0.005\n",
      "gini_zscore_reverse         -0.0420      0.014     -3.044      0.002      -0.069      -0.015\n",
      "editors_zscore              -0.0241      0.021     -1.147      0.252      -0.065       0.017\n",
      "age_zscore                  -0.0487      0.013     -3.647      0.000      -0.075      -0.022\n",
      "unique_references_zscore     0.0266      0.037      0.721      0.471      -0.046       0.099\n",
      "characters_zscore           -0.0423      0.033     -1.279      0.201      -0.107       0.023\n",
      "pageviews_zscore             0.0159      0.014      1.098      0.273      -0.013       0.044\n",
      "==============================================================================\n",
      "Omnibus:                      193.056   Durbin-Watson:                   1.923\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              416.293\n",
      "Skew:                           1.220   Prob(JB):                     4.01e-91\n",
      "Kurtosis:                       5.345   Cond. No.                         8.55\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(regOutput.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f5cdcc22",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-02T17:01:49.240830Z",
     "start_time": "2023-10-02T17:01:49.223559Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept                  -0.604147\n",
       "C(assessValueDummy)[T.1]    0.004138\n",
       "C(jobDummy)[T.1]            0.079288\n",
       "C(partyDummy)[T.1]          0.118123\n",
       "C(genderDummy)[T.1]        -0.019018\n",
       "monthlyEdit_zscore         -0.040246\n",
       "wordHuman_zscore           -0.026851\n",
       "gini_zscore_reverse        -0.042006\n",
       "editors_zscore             -0.024085\n",
       "age_zscore                 -0.048711\n",
       "unique_references_zscore    0.026580\n",
       "characters_zscore          -0.042343\n",
       "pageviews_zscore            0.015897\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regOutput.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a7ce4e63",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-02T17:01:49.951802Z",
     "start_time": "2023-10-02T17:01:49.942128Z"
    }
   },
   "outputs": [],
   "source": [
    "err_series = regOutput.params - regOutput.conf_int()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b8f8c901",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-02T17:53:32.487521Z",
     "start_time": "2023-10-02T17:53:32.475181Z"
    }
   },
   "outputs": [],
   "source": [
    "coef_df = pd.DataFrame({'coef': regOutput.params.values[1:],\n",
    "                        'err': err_series.values[1:],\n",
    "                        'varname': err_series.index.values[1:]\n",
    "                       })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7b8e7411",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-02T17:53:33.265350Z",
     "start_time": "2023-10-02T17:53:33.251142Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>err</th>\n",
       "      <th>varname</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.004138</td>\n",
       "      <td>0.063527</td>\n",
       "      <td>C(assessValueDummy)[T.1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.079288</td>\n",
       "      <td>0.069652</td>\n",
       "      <td>C(jobDummy)[T.1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.118123</td>\n",
       "      <td>0.045932</td>\n",
       "      <td>C(partyDummy)[T.1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.019018</td>\n",
       "      <td>0.053877</td>\n",
       "      <td>C(genderDummy)[T.1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.040246</td>\n",
       "      <td>0.031594</td>\n",
       "      <td>monthlyEdit_zscore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.026851</td>\n",
       "      <td>0.021793</td>\n",
       "      <td>wordHuman_zscore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.042006</td>\n",
       "      <td>0.027085</td>\n",
       "      <td>gini_zscore_reverse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.024085</td>\n",
       "      <td>0.041226</td>\n",
       "      <td>editors_zscore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.048711</td>\n",
       "      <td>0.026214</td>\n",
       "      <td>age_zscore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.026580</td>\n",
       "      <td>0.072322</td>\n",
       "      <td>unique_references_zscore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.042343</td>\n",
       "      <td>0.064987</td>\n",
       "      <td>characters_zscore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.015897</td>\n",
       "      <td>0.028428</td>\n",
       "      <td>pageviews_zscore</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        coef       err                   varname\n",
       "0   0.004138  0.063527  C(assessValueDummy)[T.1]\n",
       "1   0.079288  0.069652          C(jobDummy)[T.1]\n",
       "2   0.118123  0.045932        C(partyDummy)[T.1]\n",
       "3  -0.019018  0.053877       C(genderDummy)[T.1]\n",
       "4  -0.040246  0.031594        monthlyEdit_zscore\n",
       "5  -0.026851  0.021793          wordHuman_zscore\n",
       "6  -0.042006  0.027085       gini_zscore_reverse\n",
       "7  -0.024085  0.041226            editors_zscore\n",
       "8  -0.048711  0.026214                age_zscore\n",
       "9   0.026580  0.072322  unique_references_zscore\n",
       "10 -0.042343  0.064987         characters_zscore\n",
       "11  0.015897  0.028428          pageviews_zscore"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bf76d4c5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-02T18:00:46.424790Z",
     "start_time": "2023-10-02T18:00:46.409853Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"[1'Reference count', 2'Pageviews', 3'Page quality', 4'Character count', 5'Monthly Edits',\\n6'Gender', 7'Age', 8'Partisanship', 9'Job positions', 10'Crowd size',11'Communication intensity',\\n12'Workload diversity']\\n\""
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the order of the variables on the y axis\n",
    "\"\"\"[1'Reference count', 2'Pageviews', 3'Page quality', 4'Character count', 5'Monthly Edits',\n",
    "6'Gender', 7'Age', 8'Partisanship', 9'Job positions', 10'Crowd size',11'Communication intensity',\n",
    "12'Workload diversity']\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "66dd4543",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-02T17:57:04.243118Z",
     "start_time": "2023-10-02T17:57:04.231978Z"
    }
   },
   "outputs": [],
   "source": [
    "order = [3, 9, 8, 6, 5, 11, 12, 10, 7, 1, 4, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3269751a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-02T00:38:35.058015Z",
     "start_time": "2023-10-02T00:38:35.049148Z"
    }
   },
   "outputs": [],
   "source": [
    "#coef_df.drop('order', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9afa1851",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-02T17:57:06.248739Z",
     "start_time": "2023-10-02T17:57:06.238492Z"
    }
   },
   "outputs": [],
   "source": [
    "coef_df['order'] = order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8d40b7c0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-02T17:57:08.944900Z",
     "start_time": "2023-10-02T17:57:08.937439Z"
    }
   },
   "outputs": [],
   "source": [
    "coef_df = coef_df.sort_values(by='order')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "aa5427fd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-02T17:57:14.450301Z",
     "start_time": "2023-10-02T17:57:14.428443Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>err</th>\n",
       "      <th>varname</th>\n",
       "      <th>order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.026580</td>\n",
       "      <td>0.072322</td>\n",
       "      <td>unique_references_zscore</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.015897</td>\n",
       "      <td>0.028428</td>\n",
       "      <td>pageviews_zscore</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.004138</td>\n",
       "      <td>0.063527</td>\n",
       "      <td>C(assessValueDummy)[T.1]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.042343</td>\n",
       "      <td>0.064987</td>\n",
       "      <td>characters_zscore</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.040246</td>\n",
       "      <td>0.031594</td>\n",
       "      <td>monthlyEdit_zscore</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.019018</td>\n",
       "      <td>0.053877</td>\n",
       "      <td>C(genderDummy)[T.1]</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.048711</td>\n",
       "      <td>0.026214</td>\n",
       "      <td>age_zscore</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.118123</td>\n",
       "      <td>0.045932</td>\n",
       "      <td>C(partyDummy)[T.1]</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.079288</td>\n",
       "      <td>0.069652</td>\n",
       "      <td>C(jobDummy)[T.1]</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.024085</td>\n",
       "      <td>0.041226</td>\n",
       "      <td>editors_zscore</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.026851</td>\n",
       "      <td>0.021793</td>\n",
       "      <td>wordHuman_zscore</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.042006</td>\n",
       "      <td>0.027085</td>\n",
       "      <td>gini_zscore_reverse</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        coef       err                   varname  order\n",
       "9   0.026580  0.072322  unique_references_zscore      1\n",
       "11  0.015897  0.028428          pageviews_zscore      2\n",
       "0   0.004138  0.063527  C(assessValueDummy)[T.1]      3\n",
       "10 -0.042343  0.064987         characters_zscore      4\n",
       "4  -0.040246  0.031594        monthlyEdit_zscore      5\n",
       "3  -0.019018  0.053877       C(genderDummy)[T.1]      6\n",
       "8  -0.048711  0.026214                age_zscore      7\n",
       "2   0.118123  0.045932        C(partyDummy)[T.1]      8\n",
       "1   0.079288  0.069652          C(jobDummy)[T.1]      9\n",
       "7  -0.024085  0.041226            editors_zscore     10\n",
       "5  -0.026851  0.021793          wordHuman_zscore     11\n",
       "6  -0.042006  0.027085       gini_zscore_reverse     12"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef_df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f07dfc8e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-02T18:02:48.993187Z",
     "start_time": "2023-10-02T18:02:48.716291Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/w8/8v3lrr0d5nn4_nm9h6gr2c2w0000gn/T/ipykernel_1494/4204236210.py:19: FutureWarning: The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead.\n",
      "  y=pd.np.arange(coef_df.shape[0]), color='darkcyan')\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Define figure, axes, and plot\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4, 6))\n",
    "\n",
    "# Error bars for 95% confidence interval\n",
    "# Can increase capsize to add whiskers\n",
    "\n",
    "coef_df.plot(x='varname', y='coef', kind='barh',\n",
    "             ax=ax, color='none',\n",
    "             ecolor='darkcyan',\n",
    "             xerr='err', legend=False)\n",
    "\n",
    "ax.set_xlabel('Estimated Coefficients')\n",
    "ax.set_ylabel('')\n",
    "\n",
    "# Coefficients  s=120,\n",
    "plt.scatter(x=coef_df['coef'], \n",
    "           marker='o',  \n",
    "           y=pd.np.arange(coef_df.shape[0]), color='darkcyan')\n",
    "# reset x ticks\n",
    "plt.xticks(np.arange(-0.2, 0.3, 0.1))\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "# Line to define zero on the y-axis\n",
    "ax.axvline(x=0, linestyle='-', color='k', linewidth=0.7)\n",
    "\n",
    "ylabels = ['Reference count', 'Pageviews', 'Page quality', 'Character count', 'Monthly edits',\n",
    "'Gender', 'Age', 'Partisanship',  'Job positions', 'Crowd size', 'Communication intensity',\n",
    "'Workload diversity']\n",
    "ax.yaxis.set_ticks_position('none')\n",
    "_ = ax.set_yticklabels(ylabels, \n",
    "                       rotation=0, fontsize=9.2)\n",
    "\n",
    "#plt.savefig('.../regressionCoefUpdated.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "188393d8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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